Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labelling

TL;DR

This paper proposes a joint optimization framework for wireless crowd labelling that maximizes throughput by balancing radio and annotation resources, using advanced algorithms to solve a complex NP-hard problem.

Contribution

It introduces a novel joint optimization approach for encoding, clustering, and spectrum allocation in wireless crowd labelling, with efficient algorithms for NP-hard problems.

Findings

Significant throughput improvements over decoupled resource allocation.

Effective solutions for NP-hard joint optimization problems.

Demonstrated benefits of integrated radio and annotation resource management.

Abstract

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The realistic adoption of this technique however entails labelling of the enormous data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new perspective of wireless crowd labelling that is capable of downloading data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. In this cross-disciplinary area, the integration of the rate-distortion theory and the principle of repetition labelling for accuracy improvement gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios. Thereby, the optimal solution can be found by an efficient tree search. Next, the solution is simplified by applying truncated channel inversion. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, exact polices are derived for the annotators constrained and spectrum constrained cases. Last, simulation results demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.